During a flight test program, certain instrumented parameters are subject to degradation due to wear and tear. Over the course of a flight test program, data from several flights will be lost due to undetected instrumentation faults. The application of artificial neural networks as flight test data estimators has been proposed with the intention of reducing the aforementioned cost and wasted test flights. Several network topologies have been studied. A simulation program has been used to identify the appropriate network topology for this task. It is shown that a single network is not capable of predicting all of the correct values of the suspect parameters based solely on the information received from the reliable instruments. However, it is shown that a collection of smaller networks can succeed in this task, with each network predicting one suspect parameter. Limited training and test results based on simulation-generated data are presented. Actual flight test data from a typical business jet have been used to verify this concept and these results are presented as well. It is demonstrated that in most cases these networks are capable of predicting the measured parameter outputs with sufficient accuracy to enable identification of instrumentation system degradation.
NomenclatureC L , C D , C Y = lift, drag, and side force coefficients C h C M , C n = rolling, pitching, and yawing moment coefficients c = mean aerodynamic chord b = wingspan f(x) = neural network activation function /?, q, r = roll, pitch, and yaw rates u, v, w = velocity components along each of the axes jc, y, z = body axes a = angle of attack j8 = sideslip angle 8 A , 8 e , 8 R = aileron, elevator, and rudder deflection angles = pitch angle = bank angle 0> Subscripts B ELEV GEAR S SPOIL STAB W 0Superscript -body axes = due to elevator = due to landing gear = stability axes = due to spoiler = due to stabilizer = due to wing = aerodynamic quantity at zero angle of attack = nondimensional roll, pitch, and yaw rates
